Beta_diversity_milk
Chiara gini
December 10, 2021
Beta diversity
Between-sample variability from 16S rRna-gene sequencing data.
Clustering
PCA
Clustering by timepoint (PCA):
Clustering by treatment (PCA): after removing samples from timepoint 0 # BECAUSE WE KNOW THAT AT TIMEPOINT 0 WE SHOULD NOT HAVE DIFFERENCES BETWEEN GROUPS
## Saving 7 x 5 in imageClustering by treatment (PCA): by timepoints
Time-point 28
## Warning in data.frame(..., check.names = FALSE): row names were found from a
## short variable and have been discarded
## Saving 7 x 5 in imageTime-point 21
## Warning in data.frame(..., check.names = FALSE): row names were found from a
## short variable and have been discarded
## Saving 7 x 5 in imageTime-point 8
## Warning in data.frame(..., check.names = FALSE): row names were found from a
## short variable and have been discarded
## Saving 7 x 5 in imageTime-point 0
## Warning in data.frame(..., check.names = FALSE): row names were found from a
## short variable and have been discarded
## Saving 7 x 5 in imageMDS
Clustering by treatment (MDS): NMDS1 vs NMDS2
Clustering by timepoint (MDS):
NMDS1 vs NMDS2
Clustering by type (MDS): NMDS1 vs NMDS3
Clustering by type (MDS): NMDS2 vs NMDS3
Clustering by treatment by timepoint (MDS):
Significance of between-group distances
Significance values based on permuted analysis of variance (999 permutations), repeated 100 times.
- between treatments: p-value = 0.0569 (std = 0.0066401)
- between types: p-value = 0.0085363 (std = 0.016839)
- between treatments and type = p-value = 0.04403 (std = 0.0061191)
Below the ANOVA table from the model:
\[ X = \mu + treatment + timepoint + treatment*type + e \]
| Df | SumsOfSqs | MeanSqs | F.Model | R2 | Pr(>F) | |
|---|---|---|---|---|---|---|
| treatment | 1 | 0.3754833 | 0.3754833 | 1.702507 | 0.0256107 | 0.044 |
| timepoint | 3 | 1.2733871 | 0.4244624 | 1.924586 | 0.0868544 | 0.003 |
| residuals | 59 | 13.0122919 | 0.2205473 | NA | 0.8875348 | NA |
| total | 63 | 14.6611623 | NA | NA | 1.0000000 | NA |
going 3D
## Run 0 stress 0.0751196
## Run 1 stress 0.09009265
## Run 2 stress 0.0750855
## ... New best solution
## ... Procrustes: rmse 0.006212184 max resid 0.03108058
## Run 3 stress 0.09253849
## Run 4 stress 0.08672834
## Run 5 stress 0.07508398
## ... New best solution
## ... Procrustes: rmse 0.000255134 max resid 0.001107973
## ... Similar to previous best
## Run 6 stress 0.0766515
## Run 7 stress 0.07510223
## ... Procrustes: rmse 0.003150098 max resid 0.02039621
## Run 8 stress 0.07508376
## ... New best solution
## ... Procrustes: rmse 0.000118586 max resid 0.0005505274
## ... Similar to previous best
## Run 9 stress 0.07511047
## ... Procrustes: rmse 0.005248189 max resid 0.03104522
## Run 10 stress 0.09092367
## Run 11 stress 0.0804885
## Run 12 stress 0.07508375
## ... New best solution
## ... Procrustes: rmse 0.0001163687 max resid 0.0006493131
## ... Similar to previous best
## Run 13 stress 0.0923802
## Run 14 stress 0.07552532
## ... Procrustes: rmse 0.01300386 max resid 0.08560523
## Run 15 stress 0.07511018
## ... Procrustes: rmse 0.005201986 max resid 0.03094017
## Run 16 stress 0.07508373
## ... New best solution
## ... Procrustes: rmse 4.10306e-05 max resid 0.0002072934
## ... Similar to previous best
## Run 17 stress 0.09273423
## Run 18 stress 0.07510991
## ... Procrustes: rmse 0.005200093 max resid 0.03090325
## Run 19 stress 0.07510252
## ... Procrustes: rmse 0.003164914 max resid 0.02054252
## Run 20 stress 0.07510959
## ... Procrustes: rmse 0.005189834 max resid 0.03085674
## *** Solution reached